This program will identify the most likely aliases for a given query name, using a semi-supervised learning approach. The program will then ask the user to confirm the validity of these most likely aliases.

A fast implementation of scan statistic search for spatial overdensities. Our goal is to find rectangular regions where the count (e.g. number of disease cases) is higher than expected, given the underlying population distribution.

This program constructs a kd-tree from the contents of an input dataset of k-dimensional vectors, and then performs nearest neighbor searches within the kd-tree using query points from a query dataset.